Journal of Loss Prevention in The Process Industries最新文献

Amidst the escalating complexity of process operations, ensuring effective process safety management (PSM) is crucial to prevent major accidents. However, current PSM practices often lack the integration of evidence resources, leading to a research-to-practice gap and hindering PSM's development. To address these challenges, this study proposes evidence-based process safety management (EBPSM), which systematically integrates various evidence types such as incident reports, hazard assessment, safety audits, and scientific literature, to optimize PSM and enhance professional expertise. Firstly, the study reviews PSM development, emphasizing its shortcomings concerning safety information. Subsequently, the EBPSM approach is introduced, highlighting its necessity and key determinants. A comprehensive framework for EBPSM is presented, detailing each stage of its operational processes. To demonstrate the extended application of EBPSM, the framework is employed in liquefied natural gas (LNG) maritime safety management. Finally, the study concludes by discussing limitations and suggesting future directions for EBPSM. By emphasizing the systematic integration of evidence resources and the necessity of applying EBPSM systematically, this study aims to equip process safety practitioners with a well-informed understanding of the evidence-based approach and its potential to enhance process safety outcomes in future PSM implementations. Moreover, embracing the EBPSM approach may enable industries to proactively mitigate risks and foster a safer and more resilient working environment.

1,2,4-Triazole is a common intermediate used in the synthesis of pesticides and medicine, and has a very wide range of applications. As a kind of polynitrogen heterocycles, 1,2,4-triazole will release a lot of heat during thermal decomposition. If there is no comprehensive understanding of its pyrolysis behavior, there will be a very large thermal safety risk in the process involving 1,2,4-triazole in industrial production. At the same time, the instability of the substance may be affected by the presence of impurities, and the existing research on this aspect is quite scarce. Therefore, it is very necessary to study the effect of adding other substances on the thermal decomposition of 1,2, 4-triazole. The thermal decomposition characteristics and thermal runaway behavior of 1,2, 4-triazoles were revealed by thermodynamic experiments and various kinetic theoretical methods. According to the risk matrix, the thermal runaway danger level of 1,2, 4-triazole was determined to be level Ⅲ, which was an unacceptable risk. The results show that 1,2, 4-triazole has a huge heat release and the effect of thermal runaway is serious. In this study, three substances involved in the industrial production of fluconazole and flutriafol mixed with 1,2, 4-triazole were selected to explore their influence on the thermal decomposition of 1,2, 4-triazole under non-working conditions. DSC and ARC experiments consistently showed that these three impurities could reduce the reaction heat release of 1,2, 4-triazole to varying degrees, and confirmed that these three impurities could reduce the risk of thermal runaway of 1,2, 4-triazole to varying degrees. Among them, sodium hydride has the most outstanding performance, both in terms of reducing reaction heat release and reducing adiabatic temperature rise. The results can provide reference for loss control of 1,2,4-triazole in industrial applications.

The accurate assessment of combustible gases leaking from high-pressure storage facilities is fundamental to disaster prevention and accident rescue. The current computing methods of gas leakage commonly fail to account for the actual behavior of high-pressure gases and gas leakage resistance, leading to inaccuracies in predicting high-pressure gas leakage. This study established a gas leakage process model based on the Peng-Robinson equation of state, considering the effects of the molecular volume and intermolecular forces on the properties of high-pressure gas. Factors affecting the resistance correction coefficient of the leakage model were investigated in conjunction with the CFD simulation results to provide a formula of gas leakage resistance coefficient. The proposed model is compared with existing models and validated using experimental data. It was found that the proposed model was able to obtain more satisfactory results. The margin of error of modified leakage model remains below 12%, providing robust support for accurately evaluating the consequences of high-pressure gas leakage accidents. The current findings are of significance for the design of high-pressure gas storage and transportation systems in future.

This paper proposes an innovative approach to enrich Hazard and operability (HAZOP) analysis for complex processes using process simulators and artificial neural networks (ANNs). HAZOP study is a systematic qualitative procedure aimed at identifying potential hazards and operability issues. It heavily relies on the collective knowledge and experience of the team during brainstorming sessions. Traditionally, HAZOP considers only “one failure at a time,” overlooking the effects of deviation causes amplitudes, their propagation, and subsequent domino effects. This simplification is necessary to manage time and costs during sessions. However, in complex systems, neglecting certain scenarios may result in overlooking critical situations. In our proposed method, we leverage process simulators to simulate upset scenarios comprehensively. By systematically varying all possible deviation causes and their combinations, we generate a substantial amount of simulation data. To facilitate evaluation, we introduce novel evaluation indexes. Additionally, we define a sensitivity index for ranking HAZOP scenarios based on severity of consequences. Furthermore, we classify scenarios into three severity levels according to their consequences. To enhance HAZOP analysis, we employ ANNs. These networks learn process behaviors and predict the evaluation indexes. They also classify scenarios based on pre-simulated data. With this approach, the HAZOP team can efficiently analyze the consequences of nearly any combination of deviation causes and failures, even with varying amplitudes. We validate our method by applying it to a real-world complex polymerization plant, demonstrating its value in practical scenarios.

The effective utilization of massive safety performance data has become an urgent necessity in response to the growing complexity and scale of work safety systems. Data-driven technologies and theories have emerged as a promising avenue for enhancing safety performance management with the rapid development of big data, which urgently need to develop a comprehensive data-driven framework to continuously improve the performance of work safety systems. Firstly, the discipline support and rationale behind safety performance management in the information age are analyzed, and the research stages and strengths of data-driven are clarified. Subsequently, the three-dimensional structure of safety performance management under data-driven is depicted, the three-level data flow in the work safety process is summarized, and the data feedback process for safety performance management were described. Lastly, the overall framework of data-driven safety performance management is constructed, and the sub-frameworks are analyzed from four aspects: infrastructure, data collection and storage, data analysis, and information services. Framework advantages, development strategies, and research limitations for data-driven safety performance management are also provided. Overall, this study aims to advance the informatization and digitization construction of work safety performance management at the enterprise level through the synthesis of existing data-driven theoretical views and data analytic techniques.

The combined effects of corrosion, temperature, and pressure can cause safety issues such as shortened lifespan and failure of offshore high temperature and high pressure oil and gas well strings. This paper focuses on a certain sea area and conducts experiments on CO₂ corrosion of different string materials under different temperature and partial pressure conditions. Combined with the two-phase flow wellbore temperature and pressure-coupling model, a corrosion rate prediction model based on Back Propagation Neural Network optimized by Genetic Algorithm (GA-BPNN) is established for predicting corrosion rate along the wellbore depth. At the same time, based on the API 5C3 standard, considering the degradation effect of thermally induced metal string strength and the influence of environmental pressure, a safety evaluation model of offshore oil and gas well string based on corrosion rate prediction was established, and analysis of the change law of residual strength of the string and prediction of remaining life under the influence of different factors were carried out.

Natural disasters are unexpected natural events that cause severe property loss and moral damage. Türkiye has been exposed to many disasters due to its location. One of the disasters experienced is the Kahramanmaraş earthquake that occurred on February 6th, 2023, in the recent past. Two consecutive earthquakes caused significant loss of life and property. In addition, some technological accidents happened because of the earthquake. The biggest of these was the fire in the Iskenderun marina. After the earthquake, the big fire that started with the fire of the containers on the quay could be brought under control after a long effort and time. Although it is often overshadowed by natural disasters, technological accidents triggered by natural disasters (Natech) accidents can cause severe damage. Especially Organized Industrial Zones (OIZ) are in areas with high Natech risk. This study determined 10 criteria that will cause Natech accidents for OIZs by literature review. The Pythagorean Fuzzy Analytical Hierarchy Process (PFAHP) method is used to weight the criteria. Then, a ranking is made using The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method among the five sectors operating in Kırıkkale OIZ and which may have Natech risk.

A time-series prediction method based on AdaMax-LSTM neural network is proposed for predicting the flame propagation speed in premixed methane gas deflagration experiments, which can provide a decision-making basis for emergency operation of FLNG combustible gas deflagration accidents. Firstly, 54 sets of premixed methane gas deflagration experiments under semi-open duct obstacle conditions were conducted to investigate the different deflagration mechanisms by changing the obstacle parameters. The experimental results demonstrate that the distance between the obstacle and ignition source, obstacle length and obstacle shape will all effect the flame propagation speed and deflagration overpressure. Secondly, the LSTM neural network is employed to setup a novel method which can predict the flame speed in time series via calculating the Reynolds number and determining the turbulence of the flame accurately. The deflagration experiments results were used as the dataset for AI training for the proposed prediction method. In addition, the AdaMax optimizer is added into the backpropagation process of the proposed LSTM neural network to maximize the prediction accuracy of the method. The analysis results indicate that the AdaMax-LSTM neural network with sigmoid activation function can achieve the highest level of accuracy prediction, with the mean R² value reaching 0.95, and can identify anomaly data and the most different deflagration mechanisms experimental condition. The proposed method provides an efficient and accurate way to predict and analyze the deflagration mechanisms via employing cutting-edge AI technology.

Hydrogen is increasingly being used as an alternative renewable energy carrier because of its potential to reduce carbon emissions in transportation and heavy industry. Nevertheless, this transition necessitates establishing an infrastructure for storage, transporting, and distributing hydrogen. This infrastructure is typically equipped with pressure relief devices (PRD) to protect systems from uncontrolled pressure increases. Without PRDs, a substantial pressure increase has the potential to rupture equipment and lead to a hydrogen release, which could lead to fires, explosions, and significant damage. However, recent incidents have shown that these PRDs can also be the root cause of leaks and releases. Therefore, there is a need to understand the conditions when PRDs increase the risk versus when these devices effectively mitigate the risk. This paper presents the first comprehensive analysis of past hydrogen incidents involving PRDs and considers when these components succeed and when they fail. We then analyze a diverse, representative subset of events involving PRDs using a root cause analysis (RCA) methodology known as Tripod Beta. Further, we introduce a taxonomy breakdown of the root causes of these incidents. Finally, we provide conclusions related to the observed root causes. The most frequently observed failure modes leading to PRD incidents are 1.) spurious operations of PRDs and 2.) high input to the PRD from components upstream of the PRD, which then activate PRDs. When comparing PRD-mitigated events to PRD-initiated events, we found that PRDs initiated more events than they mitigated; however, it is yet to be seen if this is an artifact of poor (success) data reporting or a true prevalence of PRD activations which undermines their value as a protective feature. The use of Tripod Beta shows that using systematic approaches to incident investigation and analysis provides more causal insight into risk mitigation than simply documenting the occurrence of a single failure. To understand the risk tradeoff that PRD offers, we need more rigorous assessments for the risk & reliability of these components. The results of this study motivate research, design and operational changes, and upcoming codes and standards developments to ensure the continuous, safe, and reliable operation of hydrogen systems.

Safety considerations in process industries primarily revolve around process and occupational safety. In recent times, there has been a growing emphasis on the development and application of risk estimation frameworks to provide a more holistic evaluation of these critical risk types. While previous studies have offered valuable insights into the concurrent assessment and management of occupational and process hazards, a comprehensive review of frameworks and models incorporating risk estimation dimensions is lacking. Accordingly, this review aims to systematically examine studies that have devised methods to integrate the estimation of both occupational and process risks, with a focus on categorizing applied criteria for risk estimation. A thorough literature review was conducted using reputable databases, including Web of Science and Scopus, resulting in the identification of 16 studies meeting the predetermined inclusion criteria. Subsequently, the selected studies were classified into four categories: (1) New Framework Developments, (2) Existing Framework Adjustments, (3) Hybrid Frameworks, and (4) Management System/Risk Management Integration. Furthermore, employing a relevant risk dimension perspective, the identified studies were categorized based on four primary dimensions: (a) Scenario Probability, (b) Scenario Consequences, and (c) Economical Damage. It is crucial to note that the existing literature on occupational-process hazards and associated risk dimensions lacks reliable and robust studies. Therefore, further research efforts are imperative to effectively evaluate, quantify, and precisely delineate diverse risk dimensions within the integrated domain of occupational-process safety.